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import os
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
"""
_CITATION = """
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
"""
_URL = "https://github.com/google-research-datasets/wit"
_DATA_URL = "https://huggingface.co/datasets/keshan/wit-dataset/resolve/628260b88f51c831a60120d2ebc17c3475f282af/data/{language}.tar.gz"
_LANGUAGES = [
'ms',
'eu',
'si',
'ko',
'nv',
'id',
'tg',
'mn',
'fa',
'bg',
'ia',
'ca',
'jv',
'vi',
'ja',
'bs',
'te',
'war',
'hy',
'sv',
'az',
'lah',
'ht',
'sl',
'pt',
'an',
'br',
'nn',
'ceb',
'ce',
'qu',
'gl',
'fy',
'vec',
'zh',
'iw',
'vo',
'xmf',
'nds',
'bar',
'ba',
'sr-Latn',
'hsb',
'yue',
'arz',
'es',
'bn',
'de',
'mk',
'pa',
'zh-TW',
'io',
'lb',
'azb',
'ga',
'cs',
'fi',
'cv',
'sr',
'lv',
'my',
'mg',
'hu',
'it',
'kk',
'be',
'sq',
'ru',
'ar',
'cy',
'hr',
'be-tarask',
'is',
'tt',
'mr',
'ro',
'en',
'fil',
'uz',
'af',
'et',
'fr',
'no',
'ckb',
'nan',
'sw',
'la',
'lmo',
'th',
'ta',
'ast',
'eo',
'tr',
'uk',
'ur',
'ne',
'kn',
'da',
'nl',
'ka',
'pl',
'el',
'sco',
'hi',
'sk',
'oc',
'lt',
'ml'
]
class WITConfig(datasets.BuilderConfig):
"""BuilderConfig for WIT."""
def __init__(self, *args, languages, **kwargs):
"""BuilderConfig for WIT.
Args:
languages (:obj:`List[str]`): list of languages to load
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(
*args,
name="+".join(languages),
**kwargs,
)
self.languages = languages
class WIT(datasets.GeneratorBasedBuilder):
"""WIT, WIT to be used as a pretraining dataset for multimodal machine learning models."""
BUILDER_CONFIGS = [WITConfig(languages=[lang]) for lang in _LANGUAGES]
BUILDER_CONFIG_CLASS = WITConfig
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"language": datasets.Value("string"),
"page_url": datasets.Value("string"),
"image_url": datasets.Value("string"),
"page_title": datasets.Value("string"),
"section_title": datasets.Value("string"),
"hierarchical_section_title": datasets.Value("string"),
"caption_reference_description": datasets.Value("string"),
"caption_attribution_description": datasets.Value("string"),
"caption_alt_text_description": datasets.Value("string"),
"mime_type": datasets.Value("string"),
"original_height": datasets.Value("string"),#datasets.Value("int8"),
"original_width": datasets.Value("string"),#datasets.Value("int8"),
"is_main_image": datasets.Value("string"),
"attribution_passes_lang_id": datasets.Value("string"),
"page_changed_recently": datasets.Value("string"),
"context_page_description": datasets.Value("string"),
"context_section_description": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
abs_path_to_data = dl_manager.download_and_extract(
_DATA_URL.format(language=self.config.name)
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(abs_path_to_data, f'{self.config.name}/wit_v1.train.all.{self.config.name}.tsv'),
},
),
]
def _generate_examples(self, filepath):
data_fields = list(self._info().features.keys())
path_idx = data_fields.index("image_url")
with open(filepath, encoding="utf-8") as f:
lines = f.readlines()
headline = lines[0]
column_names = headline.strip().split('\t')
assert (
column_names == data_fields
), f"The file should have {data_fields} as column names, but has {column_names}"
for id_, line in enumerate(lines[1:]):
field_values = line.strip().split("\t")
# if data is incomplete, fill with empty values
if len(field_values) < len(data_fields):
field_values += (len(data_fields) - len(field_values)) * ["''"]
yield id_, {key: value for key, value in zip(data_fields, field_values)} |